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ρ i ( t ) and r i ( t ) denote the leakage delays, τ i j ( t ) and σ i j ( t ) are time-varying delays, f j , g j are neuron activation functions, u ˜ i ( t ) = − k i x i ( t k ), v ˜ i ( t ) = − l i y i ( t k ) are sampled-data state feedback inputs, t k denotes the sample time point, t k ≤ t < t k + 1, k ∈ N, ℕ denotes the set of all natural numbers.
With each event A is associated the complementary event Ac consisting of those experimental outcomes that do not belong to A. Since A ∩ Ac = Ø, A ∪ Ac = S, and P(S) = 1 (where S denotes the sample space), it follows from equation (1) that P Ac) = 1 − P(A).
where denotes the sample mean for the class.
where t n denotes the sample time, N=P × M, and P denotes the number of periods in a CIT.
In the equation above, denotes the sample period and simulates multipath delay components of the fading channel.
where (Eleft {mathbf {X}_{n - i}^{k}right }) denotes the sample mean of the past L observations.
Similar(20)
The vector denotes the samples received by the decoder at the channel output.
Denote the set of observations acquired over by =, where where denotes the sampling (data acquisition) period.
ΔT denotes the sampling interval and n is the index of the samples.
where t k denotes the sampling instant and satisfies lim k → ∞ t k = ∞.
In (17), denotes the samples vector after calibration in step 1 and the sample number for this estimation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com